130 results about "Generative Design" patented technology

The invention relates to the technical field of gift packaging three-dimensional modeling, and discloses a construction method of a gift box packaging design three-dimensional model. The method comprises the following steps: collecting natural language description, a freehand sketch image and a physical material attribute parameter; analyzing a natural language to construct a design intention map, and analyzing a sketch to generate a geometric feature code; performing knowledge alignment fusion on the two to form enhanced design semantic representation; generating an initial three-dimensional design framework by using a generative design algorithm; material parameters are integrated, compatibility simulation test is carried out, parameter self-adaptive adjustment circulation is started according to a feasibility evaluation report, and component parameters are dynamically optimized; and rendering and outputting a final model. Through deep fusion of multi-modal design information and preposed dynamic optimization of physical attributes, the accuracy of design intention expression and the producibility of a three-dimensional model are improved, and automation and intelligentization of a design process are realized.

The invention discloses a digital research and development demand automatic disassembling method based on knowledge reasoning. The method comprises the following steps: gathering multi-source demands, uniformly de-duplicating, and establishing a version baseline; a semantic graph is generated according to the standardized requirements, a support graph and a constraint set are constructed, and a resource portrait, historical measurement and a template library are formed; performing retrieval and sub-graph aggregation by using an improved GraphRAG model, completing alignment reasoning and outputting a fact set, a conflict list and a deletion list; the method comprises the following steps: generating a hierarchical task, dependency and priority by using an improved Reflexion algorithm, and performing shadow scheduling and acyclic verification to obtain an execution sequence table; generating a design / interface / test / verification / acceptance workpiece, and solving resources, milestones and key paths according to the design / interface / test / verification / acceptance workpiece; and a tracing link and an early warning mechanism are established, and rearrangement and resource redistribution are carried out when the time exceeds the limit. According to the method, the schedule forming time is shortened, the reworking and delay risks are reduced, and the workpiece coverage rate, the critical path controllability and the delivery traceability are improved.

The invention discloses a multi-objective optimization method for the form of an urban block, and the method comprises the following steps: S100, constructing a parameterized model, carrying out the experimental design, defining a group of geometric parameters used for describing the form of the urban block, and generating a plurality of sample points in the value range of the geometric parameters through employing a sampling method; s200, bidirectional coupling numerical simulation is executed, a data set is generated, at least one thermal comfort evaluation index and one air quality evaluation index are output, and therefore the data set containing input parameters and output indexes is formed; step S300, constructing and verifying a machine learning agent model to obtain an agent model capable of rapidly predicting the evaluation index; step S400, executing multi-objective optimization search, and automatically searching to enable the thermal comfort evaluation index and the air quality evaluation index to simultaneously achieve a better Pareto optimal solution set; and step S500, analyzing and outputting an optimization result, extracting an optimization value range of the geometric parameters, and generating a design guide.

Variants of the method can include determining a design, generating a geometric representation of the design, generating a topological representation of the design, determining a context value set, predicting a physical field using a trained physics model, optionally verifying the physical field, optionally generating a corrected physical field, and optionally training the physics model based on the corrected physical field.

The invention relates to the field of motor structure design and optimization, and discloses a cogging torque and torque ripple optimization method for a permanent magnet motor for a vehicle. The method comprises the steps that based on an electromagnetic scheme, a geometric boundary and a term dictionary, a parameterized geometric prototype containing four symmetric semi-arc auxiliary grooves is constructed, and a design variable set structure is generated; converting the structure into a target evaluation interface structure containing a target item and a constraint item; an improved adaptive particle swarm algorithm is combined with finite element rapid check and a new iteration function to carry out optimization solution, and an updated candidate group structure is generated; and finally, optimal parameters are screened through Pareto sorting and robustness recheck, and an engineering configuration structure is generated. According to the method, through systematic parameter modeling, an efficient collaborative optimization algorithm and simulation evaluation, the optimization efficiency and reliability are remarkably improved, the cogging torque and the torque ripple of the motor are effectively reduced, and meanwhile, the process feasibility and the performance robustness of a design scheme are ensured.

The invention discloses a Solar-Think photovoltaic power station intelligent construction platform, and the platform comprises a photovoltaic power station intelligent design module which is used for automatically generating design data; the photovoltaic power station construction collaboration module is used for converting a design model in the design data into a digital twin model, collecting construction data in real time through Internet of Things equipment, and dynamically updating a construction state in a virtual space; the predictive operation and maintenance module is used for predicting equipment faults through an LSTM neural network based on design parameters, construction records and real-time operation and maintenance data of the design model and generating an optimal maintenance path in combination with the digital twinborn model; and the photovoltaic power station full-process data closed-loop architecture module is used for automatically synchronizing the design model to the construction collaboration module, reversely correcting the design model based on construction data, creating association between the construction data and the operation and maintenance data, forming an equipment full-life-cycle file, and transmitting the equipment full-life-cycle file to the construction collaboration module. And feeding back the operation and maintenance data to the design module to support next-generation power station scheme optimization.

The invention provides a reverse generation type design method and system of a battery material based on a large language model. The method comprises the following steps: carrying out serialized representation on a crystal structure of a lithium ion battery material; constructing a material basic model, and training the material basic model based on the serialized representation result to obtain a trained material basic model; a multi-dimensional attribute virtual KV attention mechanism is introduced into the material basic model, and a property conditional generation model is obtained; accessing a property prediction head network to the material basic model, and performing supervised training on the material basic model to obtain a material property prediction model; and performing reinforcement learning training on the property conditional generation model to obtain an optimized property conditional generation model, and generating a crystal structure candidate set based on the optimized property conditional generation model. By means of the method, efficient, controllable and diversified generation of the hundred-atomic-scale complex crystal structure is achieved, and the precision and practicability of material reverse design are remarkably improved.

The invention relates to the technical field of offshore wind power equipment, and discloses an offshore floating type wind turbine mooring system optimization method based on a proxy model, and the method comprises the following steps: S1, determining key design parameters; s2, sampling to generate design points; s3, establishing a total index function of multi-target coupling; s4, generating a sample database through numerical simulation; s5, constructing an agent model by using a Kriging method; s6, calling the proxy model to search an optimal solution by adopting a differential evolution algorithm; s7, performing numerical simulation verification on the optimal solution and judging convergence; and S8, if not, updating the database and returning to S5 for iteration, and if so, outputting the optimal configuration. According to the method, the problems that a traditional optimization method is high in simulation calculation cost and too long in consumed time are solved, searching is carried out through the proxy model instead of high-precision simulation, the verification iteration step is combined, the optimization efficiency is greatly improved while the result accuracy is guaranteed, and the multi-target mooring scheme with the optimal comprehensive performance can be efficiently obtained.

The invention discloses a design parameter collaborative management method and device for a BIM platform, and relates to the technical field related to BIM, and the method comprises the steps: importing a forward design scheme of a thermal power plant into the BIM platform, and generating a design model; performing multi-dimensional accident tracing according to the design model to obtain an accident association map; obtaining a design adjustment space based on the demand and the map adjustment scheme; performing multi-dimensional risk prediction based on the BIM platform and the risk model to generate a risk prediction map; optimizing the adjustment space based on the prediction map and the risk constraint; through disturbance injection optimization, a final forward design optimization result is obtained, so that the technical problems of low design efficiency and difficulty in guaranteeing design quality caused by dispersed design parameters and insufficient collaboration of the thermal power plant in the forward design process of the traditional thermal power plant are solved, and multi-dimensional accident fitting tracing, risk prediction, optimization and other means are realized. The collaborative management level and design quality of the forward design parameters of the thermal power plant are improved, and the accident risk is reduced.

The invention relates to the technical field of electric power engineering, in particular to an electric power construction acceptance method and system based on an image processing technology, and the method comprises the steps: receiving an electric power construction drawing, recognizing an electric power equipment mark and an association mark through a deep neural network, and generating a design parameter set; a multi-view depth image of a construction site is collected, point cloud data and topology rules are fused to construct a live-action three-dimensional model, and an actual measurement parameter set is extracted; carrying out space coordinate system alignment on the design space coordinates and the actually measured space coordinates, calculating size and angle deviations and forming a comprehensive deviation data set; and performing compliance judgment according to a safety threshold value of the power equipment, marking a standard exceeding position, generating a deviation thermodynamic diagram, generating a dynamic rectification scheme in combination with a historical acceptance database and construction environment information, and finally outputting a power acceptance report containing the thermodynamic diagram and rectification suggestions. According to the invention, automatic identification and intelligent rectification of the power construction deviation can be realized, and the acceptance efficiency and the quality traceability are improved.

The invention relates to the technical field of data security, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a security processing method, device and equipment oriented to software development and a medium. Obtaining an architecture design document to identify potential safety hazards and generate design improvement suggestions; generating a code based on the business logic description and the design improvement suggestion, and completing security detection and repair to obtain a processed code and a code repair record; performing security test on the processed code and recording a test result; collecting data of exception identification, hidden danger identification, code detection and repair and security test to update the security knowledge base; and generating a security analysis report based on the demand exception list, the design improvement suggestion, the code repair record and the test result. According to the invention, through a security identification and restoration process from demand to test, early discovery of security problems, linkage processing and knowledge self-updating are realized.

Embodiments of the present disclosure provide a system for automatically generating design rule checking (DRC) code for a design rule. The DRC code is generated using a first large-language model (LLM) agent and a second LLM agent. The first LLM agent generates a plurality of design rule conditions based on a multi-modal description of the design rule. The second LLM agent generates executable DRC code based on the plurality of design rule conditions. The executable DRC code is executed on example layouts to generate corresponding outputs. The executable DRC code is evaluated by comparing the outputs with corresponding reference outputs. The executable DRC code is iteratively regenerated based on the evaluation, where the evaluation includes providing a performance report to the first LLM agent and / or to the second LLM agent.

An acetabular bone defect filling block design method based on a force conduction path comprises the steps that a mechanical conduction target path is determined, specifically, the stress condition of a hip joint in a gait cycle is simulated on the basis of a three-dimensional model of a pelvis of a patient, a bone defect area Vdefect and a three-dimensional model of an implanted supporting bracket which are obtained in advance, and the mechanical conduction target path is determined; determining an expected force flow conduction path from the acetabular cup loading area to the host bone main bearing area; a filling space determining step: calculating a residual defect space Vremain formed after the support is implanted; a filling block main body generation step: taking the residual defect space Vremain as a design boundary, taking following of the expected force flow conduction path and realization of matching of the structural rigidity and the host bone as an optimization target, generating a filling block main body three-dimensional model with an internal support truss through a topological optimization algorithm, and steering a strong gluteus column by depending on an incomplete acetabular ring, and a fixed foundation which cannot be shaken is provided. An intelligent structure with the optimal mechanical property is generated by converting empirical shape matching into simulation driving generation type design.

Described herein is a computer implemented method, a system and non-transitory computer readable memory for automatically generating a design. The method includes receiving an input prompt for generating the design and generating a design background based at least on the input prompt and a seed design template. The method further includes generating design text based at least on the input prompt; generating a design record including an identifier of the generated design background and the generated design text; and causing the design to be displayed on a client device, the design rendered based on the design record.

The application discloses a kind of jewelry 3D model design method and system, comprising: obtaining historical jewelry design full modal data;Multi-dimensional feature extraction and association labeling are carried out to the full modal data using computer vision, natural language processing technology, to construct three-dimensional feature matrix;According to the three-dimensional feature matrix, through cross-modal structured labeling, to construct cross-modal association database;Using cross-modal association database, through multi-task deep learning model, generate multi-modal aesthetic feature database;Using multi-modal aesthetic feature database, train multi-task AI aesthetic evaluation model that fuses geometry, style, user preference;Through graphical interface, jewelry 3D design data is obtained, and using multi-task AI evaluation model, the quantitative evaluation of 3D design is carried out, to generate design evaluation result.Through the design method provided by the application, the objectivity of jewelry 3D model design evaluation can be improved, so as to improve the design efficiency and design quality.

The application belongs to the field of product generative design, and discloses a UAV form design method and device based on emotion driving and a medium. The method comprises the following steps: collecting user comment data, and extracting and screening core emotional words by using a BERTopic model; obtaining visual attention data of users on different UAV form components through an eye movement experiment, and calculating objective importance weight of each form component by combining an entropy weight-TOPSIS method; introducing the objective importance weight as prior knowledge into a deep learning model, constructing a Transformer-BiLSTM model fused with the prior weight, establishing a mapping relationship between user emotion and key UAV form, and predicting and generating an optimal form combination sketch; generating the sketch into a high-fidelity rendering image by using a stable diffusion model, combining physiological signals collected through eye movement and electroencephalogram multi-modal experiments, objectively verifying and optimizing the generated scheme, and determining an optimal design scheme, so that intelligent conversion from abstract emotional semantics to concrete product form is realized, and product design efficiency is improved.

The invention relates to the technical field of aero-engine overall design, and discloses an aero-engine generation design method and system based on fusion implicit empirical reasoning, and the method comprises the steps: generating an initial thermal scheme population at a thermal parameter space sampling boundary, and carrying out the optimization through a multi-objective evolutionary algorithm, decision feature vectors including multi-dimensional physical and engineering attributes such as thermal load, cost and service life are constructed, and a related engineering knowledge base is retrieved in combination with an RAG technology, so that downstream structure design is not blind trial and error any more, but can actively adapt to upstream top layer decisions, and the engineering rationality of a design scheme is greatly improved; besides, standard actions of'positive and negative experience focusing / protocol + multi-target optimization 'are introduced into a performance layer and a structure layer, and invalid spaces are greatly eliminated in the initial stage of design, so that the problem of'dimensionality disaster' in a high-dimensional design space is solved, and the generation efficiency is remarkably improved.

The invention relates to the technical field of structural engineering optimization design and intelligent model selection, in particular to a structured generative adversarial network-based pipe truss structural design and model selection method, which comprises the following steps of: obtaining pipe truss load data and corresponding structural design schemes under different load conditions; respectively coding the load data and the design scheme to obtain a multi-dimensional dynamic load feature vector and graph structure information; inputting the two schemes into a generator, and generating a design scheme and a model selection scheme by taking the minimum steel use amount as a target under the constraint of physical information; discriminating the generation scheme and the real scheme through a discriminator, calculating an adversarial loss, and alternately training the generator and the discriminator until convergence; and finally, inputting the preset load and the structure design scheme into a convergence generator, and outputting an optimal scheme. According to the method, the end-to-end mapping from the dynamic load to the optimal design and model selection scheme is realized, the design efficiency is remarkably improved, and the structural lightweight is realized while the engineering reliability is ensured.

Variants of the method can include determining a design, generating a geometric representation of the design, generating a topological representation of the design, determining a context value set, predicting a physical field using a trained physics model, optionally verifying the physical field, optionally generating a corrected physical field, and optionally training the physics model based on the corrected physical field.

The present application relates to the field of industrial software, and provides a chemical process automatic generation method, system and medium. The chemical process automatic generation method comprises the following steps: receiving a natural language description about an industrial process, wherein the natural language description comprises design constraints of the industrial process; starting a multi-agent collaboration system, converting the natural language description into an intermediate representation of the design process through closed-loop interaction among the multi-agents, wherein the multi-agent collaboration system utilizes a semantic entropy index to monitor the logical determinacy of each generation node in the process of generating the design process and triggers a real-time correction mechanism according to the uncertainty quantification result; mapping the intermediate representation into executable instructions of a heterogeneous simulation engine through an adaptation interface to drive the heterogeneous simulation engine to perform physical simulation calculation; and asynchronously extracting the calculation result of the heterogeneous simulation engine to realize closed-loop verification of the design process.

This invention discloses a SysML-based visual modeling workflow configuration method, comprising the following steps: creating system design stages and sub-stages through a visual interactive interface, and setting operation items for each sub-stage; establishing a dynamic association mapping relationship between operation items and system modeling language model elements based on the semantic association method of the system modeling language, and converting it into an independent configuration file; automatically analyzing the upstream and downstream dependencies of operation items based on the configuration file; monitoring the execution status of each operation item in real time, and dynamically updating the execution status of sub-stages and stages according to the status; automatically providing recommended views and model elements for operation items, and generating design deliverables; automatically performing semantic association analysis of data conflicts; and recording the modification history of model elements for traceability management of design results. This invention provides an efficient, flexible, and traceable modeling workflow configuration method, significantly improving the accuracy and collaboration efficiency in the system design process.

The present application relates to the technical field of gift packaging three-dimensional modeling, and discloses a kind of construction methods of gift box packaging design three-dimensional model.The method includes collecting natural language description, hand-drawing sketch image and physical material attribute parameter;Analysis natural language construction design intent graph, analyze sketch to generate geometry feature code;The knowledge alignment fusion of the two forms enhanced design semantic representation;Initial three-dimensional design framework is generated using generative design algorithm;Integrate material parameters and carry out compatibility simulation test, start parameter self-adaptive adjustment cycle according to feasibility evaluation report, dynamically optimize component parameters;Render output final model.Through the deep fusion of multi-modal design information and the preposition of physical attributes dynamic optimization, the accuracy of design intent expression and the producibility of three-dimensional model are improved, and the automation and intelligentization of design process are realized.

The application discloses a kind of personalized design intelligent interaction methods based on topology optimization, including S1, construct the user behavior monitoring system based on multi-modal data acquisition, generate high-dimensional data set containing time sequence characteristics;S2, based on adaptive deep neural network model, dynamically predict and hierarchical modeling to user personalized demand;S3, the optimal solution of design parameter is generated in real time by dynamic topology reconstruction algorithm;S4, through reinforcement learning mechanism, iteratively update design scheme generation rule;S5, construct neural network supported multi-objective design space exploration and optimization framework, quickly filter and optimize design parameter;S6, through multidimensional data mapping and parameterization control means, realize the dynamic update and local optimization of design scheme;S7, establish data-driven continuous learning and evolution mechanism, constantly optimize user demand prediction model and design optimization algorithm.The application has the advantages of strong dynamic adaptability, high intelligent level and high personalized satisfaction precision.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating graphic design layouts using a machine learning model to modify a layout according to content conditions. For example, the disclosed systems receive, from a client device, one or more content conditions defining parameters for generating design layouts. In some embodiments, the disclosed systems generate a set of fused features from the one or more content conditions. In certain embodiments, the disclosed systems encode, utilizing a machine learning, a content-conditioned layout embedding from the set of fused features. In some embodiments, the disclosed systems generate, from the content-conditioned layout embedding utilizing the machine learning model, a design layout comprising layout parameters defined by the one or more content conditions.

The system according to this embodiment aims to enable seamless changes to building designs during business negotiations. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a verification unit, and a provision unit. The reception unit receives customer requests. The generation unit generates design drawings based on the requests received by the reception unit. The verification unit verifies whether the design drawings generated by the generation unit comply with the law. The provision unit provides the design drawings generated by the generation unit to the customer.

Described herein is a computer implemented method for generating a plurality of synthesised designs based on a selected design that includes a first element. The method includes accessing design generation data that includes source data and field association data. The source data defines a first field and a first design generation record that defines a first content item that is an image-type content item and that is associated with the first field. The field association data defines a first association between the first field and the first element. The method further includes generating a first synthesised design that is based on the selected design and that is generated such that the first element of the selected design is updated in the first synthesised design using the first content item.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes, where the three dimensional (3D) models of the physical structures are produced in accordance with a design criterion that limits a minimum thickness of the generatively designed 3D models, include: obtaining a design space for an object to be manufactured and one or more design criteria including a thickness constraint; iteratively modifying a generatively designed 3D shape of the modeled object in the design space in accordance with the one or more design criteria, including measuring a current thickness for the 3D shape using an overall relationship of a volume of the 3D shape with respect to a surface area of the 3D shape; and providing the generatively designed model for use in manufacturing the physical structure using one or more computer-controlled manufacturing systems.

Described herein is a computer implemented method. The method includes receiving, via one or more user input devices, a design request that includes design description that describes a design. The design request is processed to generate an action generation prompt and a set of output actions is generated by processing the action generation prompt using a first trained machine learning model. A new design is generated based on the set of output actions, and generating the new design includes processing the set of output actions to generate a corresponding set of design elements and including the set of design elements in the new design.

We provide the system. [Solution] A means of receiving information from users about their lifestyle, family structure, hobbies, and future life plans, Based on the aforementioned information, a means for automatically generating a design plan using artificial intelligence, A means for modeling and visualizing the aforementioned design plan in a virtual space using three-dimensional modeling means, A means by which the user experiences a three-dimensional model in the virtual space and provides a list of selected products and requests for desired changes regarding the interior design, Based on the aforementioned change requests, a means of using artificial intelligence to execute product suggestions and design changes in real time, A system that includes this.